Tao Wang
Assistant Professor
Department of Software and Information Systems, University of North Carolina at Charlotte
Woodward Hall 341, 9201 University City Blvd, Charlotte, NC 28223
Email: [email protected]      
Office: Woodward Hall     



Interested in working with us? I am always looking for capable, dedicated, and self-motivated students to involve in my research. Multiple funded positions are available for both Ph.D. and undergraduate students. For detailed information, please refer to Prospective Students.

News

[Aug, 2023] The paper on preventing data-oriented attacks through Intel SGX escorted data integrity has been accepted for IEEE CNS 2023.
[Feb, 2023] I will serve on the technical program committee of IEEE Conference on Communications and Network Security 2023.
[Dec, 2022] Our "WeCARE" project towards securing wireless IoT (WIoT) networking is funded by the Office of Naval Research. (Sole PI)
[Nov, 2022] The project on trustworthy and resilient energy internet of things (eIOT) has been funded by The U.S. Department of Energy (PI: Dr. Misra).
[Aug, 2022] Our work on examining the security and privacy of third-party JavaScript caching is accepted to IEEE CNS 2022.
[Mar, 2022] My proposal towards IoT confidentiality is funded by NSF Engineering Research Initiation (ERI) award. Thank you, NSF! (Sole PI)
[Mar, 2022] Our project in collaboration with Northeastern University is funded by U.S. Army Research Laboratory.
[Mar, 2022] Our work on dynamic and lightweight data-channel coupling has been accepted to ACM WiSec 2022. Congratulations, Shengping!
[Feb, 2022] I will serve on the technical program committee of IEEE Conference on Communications and Network Security (CNS) 2022.
[Jan, 2022] The work on proactive anti-eavesdropping is to appear in IEEE Transactions on Dependable and Secure Computing (TDSC).
[Dec, 2021] The paper on examining user selection in MU-MIMO networks is accepted to IEEE INFOCOM 2022. Congratulations, Shengping!
[Dec, 2021] Interviewed by Las Cruces Sun-News on ransomware attacks and potientional countermeasures. Here is the Link of the article.
[Nov, 2021] Our work on deceiving machine learning based IoT device fingerprinting has been accepted to IEEE DySPAN 2021.
[Oct, 2021] I will serve on the technical program committee of IEEE International Conference on Computer Communications and Networks (ICCCN) 2022.
[Jul, 2021] The paper on combating adversarial network inference has been accepted by IEEE/ACM Transactions on Networking (ToN).
[Oct, 2020] I will serve on the technical program committee of IEEE International Conference on Communications (ICC) 2021.
[Dec, 2019] Our work on proactive network topology obfuscation is to appear in IEEE INFOCOM 2020.
[Nov, 2019] The paper on inferring user activities from encrypted wireless traffic won the Best Paper Award at IEEE GlobalSIP.
[Oct, 2019] Our paper "Far Proximity Identification in Wireless Systems" has been accepted by IEEE Transactions on Dependable and Secure Computing (TDSC).
[Nov, 2018] Our paper on evesdropping entrapment has been accepted by IEEE INFOCOM 2019.

Short Biography

Dr. Tao Wang is an Assistant Professor in Department of Software information Systems at UNC Charlotte. He received his Ph.D. degree from University of South Florida under the supervision of Dr. Yao Liu in 2019, and received his B.S. degree from Jilin University, China. His research focuses on network and cyber-physical security with an emphasis on designing defense methods that can protect emerging wireless technologies (e.g., IoT, 5G network) from being undermined by adversaries. Recently, he has been working on the adversarial machine learning towards secure resource allocation in Multi-user MIMO systems. His research is also related to web security, specifically, on identifying security threats and developing countermeasures to improve web security.

Research Interests

  • Network Security, Web Security, 5G Security, Cyber-physical System Security and IoT Security
  • Wireless Networking and Wireless Communication
  • Machine Learning for Cybersecurity, Trustworthy Machine Learning, and Adversarial Machine Learning

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